C Mallika

About C Mallika

I am a Senior Data Engineer with 8+ years of experience designing and implementing scalable, high-performance data platforms across Azure, AWS, and Hadoop ecosystems. I specialize in building robust ETL/ELT pipelines, real-time streaming architectures, and enterprise-grade cloud data solutions that power analytics, BI, and advanced data science workloads.Throughout my career, I have worked with large-scale distributed systems handling billions of records and TB-scale datasets. I have successfully migrated legacy on-premises systems to modern cloud architectures while improving pipeline efficiency by 20–35% and reducing deployment time through CI/CD automation.

Experience

1. Senior Data Engineer

   CVS Health

   Feb 2025 — Present · US

   Designed and maintained scalable ETL/ELT pipelines improving performance by 30% across multiple workloads.• Integrated data from on-prem (MySQL, Cassandra) and cloud (Blob, ADLS, Azure SQL) into Snowflake for enterprise analytics.• Developed Spark (Scala/PySpark) pipelines for batch and streaming workloads handling billions of records.• Implemented CI/CD automation using Azure DevOps and Jenkins, reducing deployment time by 40%.• Migrated legacy Oracle systems into Azure Synapse and ADLS with zero-downtime cutover.• Built Azure Functions & Logic Apps for automated ingestion, triggers, and orchestration flows.• Developed Python scripts for data validation, file processing, and ingestion automation.• Improved SQL queries, stored procedures, and indexing strategies resulting in 25% faster execution.• Built Kafka → Spark → Hive ingestion pipelines enabling near real-time analytics.• Created high-quality data models in Snowflake (SCD, fact/dimension, surrogate keys).• Designed and optimized Databricks clusters for better memory utilization and computer efficiency.• Implemented robust data quality checks, audit logs, and error-handling frameworks.• Collaborated with cross-functional teams in Agile sprints, PI planning, and release cycles.• Supported production issues, optimized workloads, and strengthened system reliability.• Environment: Azure Data Factory, Databricks, Snowflake, Azure SQL, ADLS, Kafka, Spark, Hive, Python, Scala

Education

• Indiana Institute of Technology

  Master's degree, Information Science/Studies